Awesome Papers: 2016-12-2

Bayesian Optimization for Machine Learning : A Practical Guidebook

Ian Dewancker, Michael McCourt, Scott Clark

The engineering of machine learning systems is still a nascent field; relying
on a seemingly daunting collection of quickly evolving tools and best
practices. It is our hope that this guidebook will serve as a useful resource
for machine learning practitioners looking to take advantage of Bayesian
optimization techniques. We outline four example machine learning problems that
can be solved using open source machine learning libraries, and highlight the
benefits of using Bayesian optimization in the context of these common machine
learning applications.

Improving Scalability of Reinforcement Learning by Separation of Concerns

Harm van Seijen and Mehdi Fatemi and Joshua Romoff

In this paper, we propose a framework for solving a single-agent task by
using multiple agents, each focusing on different aspects of the task. This
approach has two main advantages: 1) it allows for specialized agents for
different parts of the task, and 2) it provides a new way to transfer
knowledge, by transferring trained agents. Our framework generalizes the
traditional hierarchical decomposition, in which, at any moment in time, a
single agent has control until it has solved its particular subtask. We
illustrate our framework using a number of examples.